Traffic accident severity prediction and cognitive analysis using deep learning

The paper presents a comparison between two modeling techniques, Bayesian network and Regression models, by employing them in accident severity analysis. Three severity indicators, that is, number of fatalities, number of injuries and property damage, are investigated with the two methods, and the major contribution factors and their effects are identified. The results indicate that the goodness of fit of Bayesian network is higher than that of Regression models in accident severity modeling. This finding facilitates the improvement of accuracy for accident severity prediction. Study results can be applied to the prediction of accident severity, which is one of the essential steps in accident management process. By recognizing the key influences, this research also provides suggestions for government to take effective measures to reduce accident impacts and improve traffic safety.

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Real-Time Traffic Accident Severity Prediction Using Data Mining Technologies

2017 International Conference on Network and Information Systems for Computers (ICNISC) ◽

10.1109/icnisc.2017.00059 ◽

2017 ◽

Author(s):

Xiao-Ling Xia ◽

Bing Nan ◽

Cui Xu

Keyword(s):

Data Mining ◽

Real Time ◽

Traffic Accident ◽

Real Time Traffic ◽

Severity Prediction ◽

Accident Severity ◽

Using Data

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An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm

Journal of Advanced Transportation ◽

10.1155/2020/1257627 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jing Gan ◽

Linheng Li ◽

Dapeng Zhang ◽

Ziwei Yi ◽

Qiaojun Xiang

Keyword(s):

Machine Learning ◽

Traffic Safety ◽

Traffic Accident ◽

Learning Algorithm ◽

Sustainable Transportation ◽

Machine Learning Algorithm ◽

Statistical Regression ◽

Road Traffic Safety ◽

Severity Prediction ◽

Accident Severity

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

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RFCNN:Traffic Accident Severity Prediction based on Decision Level Fusion of Machine and Deep Learning Model

IEEE Access ◽

10.1109/access.2021.3112546 ◽

2021 ◽

pp. 1-1

Author(s):

Mubariz Manzoor ◽

Muhammad Umer ◽

Saima Sadiq ◽

Abid Ishaq ◽

Saleem Ullah ◽

...

Keyword(s):

Deep Learning ◽

Learning Model ◽

Decision Level ◽

Severity Prediction ◽

Accident Severity ◽

Decision Level Fusion ◽

Deep Learning Model ◽

Level Fusion

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Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2014-0023 ◽

2014 ◽

Vol 4 (1) ◽

pp. 31-42 ◽

Cited By ~ 9

Author(s):

Tatiana Tambouratzis ◽

Dora Souliou ◽

Miltiadis Chalikias ◽

Andreas Gregoriades

Keyword(s):

Traffic Accident ◽

Prediction Models ◽

Scatter Search ◽

Principal Component ◽

Correct Prediction ◽

Information Mining ◽

Severity Prediction ◽

Accident Severity ◽

Republic Of Cyprus ◽

Selection Of

Abstract The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches

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